A photograph of a fruit store with a women selecting fruit. The woman is bounded in a white box to show how SageMaker Object Detection algorithm works.

Object Detection Algorithm

The SageMaker Object Detection algorithm identifies and classifies objects in images. The identified object is placed in a class with a numerical measure of confidence. The location in the image is identified by a bounding box around the object. Object Detection is a Supervised Learning algorithm trained on a corpus of labeled images.

Because the Object Detection algorithm returns the location of the object on the image it is possible to process the output further. This leads to richer use cases where more information is extracted from the object. Examples include reading bar codes, identifying product items and determining the state of an object i.e. is it defective, or unsafe.

Attributes

Problem attributeDescription
Data types and formatImage
Learning paradigm or domainImage Processing, Supervised Learning
Problem typeObject detection and classification
Use case examplesDetect people and objects in an image

via Gfycat

Training

The algorithm can be trained from scratch or by models from pre-trained on the ImageNet data. The recommended format for training data is the Apache MxNet recordIO format, although it will also accept jpeg and png.

To speed up training you can seed the training data with data from a model you trained previously. This is called incremental training.

Model artifacts and inference

DescriptionArtifacts
Learning paradigmSupervised Learning
Request formatRecommended: application/x-image; Also jpeg, png
Resultapplication/x-image

Processing environment

Both CPU and GPU instances can be used in single or multi-instance configurations. The GPU instance can have multiple GPUs.

AWS AI-ML Partner Deep Dive Webinar: Object Detection and Image Classification Algorithms

This is a one hour and 9 minute video from AWS.

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